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Record W4401996283 · doi:10.1075/sar.23001.fed

Words worth noticing

2024· article· en· W4401996283 on OpenAlexaffabout
Richard M. Feddersen, John L. Plews, Kim Misfeldt

Bibliographic record

VenueStudy Abroad Research in Second Language Acquisition and International Education · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychologyComputer scienceLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

Abstract We use guided language learning journals written in second language (L2) German by Canadian undergraduates while studying in Germany and apply structural linguistic analyses and Cognitive Discourse Analysis to examine lexical items students attend to and, drawing on Bloom’s taxonomy, trace their learning process through their narrative language use. We show that lexical acquisition in study abroad is diverse and complex, observing a preference for meaning over function, and attention to items that were highly specialized or personally meaningful. Students also engaged in what we call private languaging in a safe environment serving to engage language to think through and construct meanings as planning for future interaction. We shed light on the process of language development during study abroad, refine quantitative understandings of L2 gains in previous studies through our qualitative approach, and expand our knowledge of how to assist learners to optimize the personal linguistic benefits of studying abroad.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.174
Threshold uncertainty score0.582

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1740.062

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.070
GPT teacher head0.444
Teacher spread0.374 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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